A ‘Pre-Flight Checklist’ for Machine Learning Training Data

Machine learning is often key to success for today’s institutions that rely heavily on data. But often, data science teams can have a difficult time convincing their organizations of the breadth and size of a training data challenge.

That’s according to a new white paper from Alegion that serves as a blueprint for preparing your own machine learning training data for your enterprise.

According to Alegion, the first few steps involved in winning approval for a machine learning project, like initial modal training, doesn’t require a lot of data. But the next steps can be much harder.

“Now the team must expose the algorithm to more — often many more — use cases. The stakes are high. The model can’t go into production if it isn’t able to navigate the greater complexity and diversity of this second stage,” the new report states.

One of the obstacles with machine learning training data is that you can count on each additional use case requiring as much, or more data than the single use case in the proof of concept.

“For example, when clients ask us to prepare the training data required to get to ROI, it is not uncommon for us to label and annotate hundreds of thousands or even millions of data items,” Alegion points out.

Alegion’s new report acts as a “pre-flight checklist” for data science teams that are contemplating preparing their own machine learning training data. (Photo: Shutterstock/MY stock)

The company’s new report acts as a “pre-flight checklist” for data science teams that are contemplating preparing their own machine learning training data. The checklist can then serve as a tool to measure enterprises’ level of preparedness for this type of endeavor.

Alegion explains when interacting with clients, it often encounters similar scenarios. The project is often highly visible within the company, data science teams are trying to get the model to a level of confidence that will let them to put it into production, and they’re preparing the training dataset themselves — witch can be an overwhelming task. Sometimes, this results in going over budget, and falling behind schedule.

That said, there is a structure and checklist Alegion contends makes it easier to address creating machine learning data. This includes steps covering tools, people and skills. For example, do you have a task and workflow management platform? Or, do you know how many data specialists you need? Does your team have task and workflow design skills?

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